Overview

Dataset statistics

Number of variables32
Number of observations1345
Missing cells800
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory754.3 KiB
Average record size in memory574.3 B

Variable types

CAT17
NUM8
BOOL7

Warnings

Killed Motorist has constant value "1345" Constant
Killed passengers has constant value "1345" Constant
Killed Cyclists has constant value "1345" Constant
Killed Pedestrians has constant value "1345" Constant
Total Killed has constant value "1345" Constant
Motorcycle has constant value "1345" Constant
Taxi vehicle has constant value "1345" Constant
Location 1 (city) has constant value "1345" Constant
Location 1 (state) has constant value "1345" Constant
Intersection Address has a high cardinality: 1328 distinct values High cardinality
Contributing Factors (**) has a high cardinality: 105 distinct values High cardinality
Location 1 has a high cardinality: 1248 distinct values High cardinality
PersonsInvolved(*) is highly correlated with Number of AccidentsHigh correlation
Number of Accidents is highly correlated with PersonsInvolved(*)High correlation
Bicycle is highly correlated with Injured CyclistsHigh correlation
Injured Cyclists is highly correlated with BicycleHigh correlation
Contributing Factors (**) has 796 (59.2%) missing values Missing
Intersection Address is uniformly distributed Uniform
Location 1 is uniformly distributed Uniform
Injured Motorist has 1170 (87.0%) zeros Zeros
Injured Passengers has 1171 (87.1%) zeros Zeros
Total Injured has 940 (69.9%) zeros Zeros
Other has 1306 (97.1%) zeros Zeros
Passenger vehicle has 269 (20.0%) zeros Zeros
SUV\Station Wagon has 923 (68.6%) zeros Zeros

Reproduction

Analysis started2020-12-12 20:15:18.825122
Analysis finished2020-12-12 20:15:25.750081
Duration6.92 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Intersection Address
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1328
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
EAST 184 STREET and JEROME AVENUE
 
2
EAST GUN HILL ROAD and GUNTHER AVENUE
 
2
EAST 168 STREET and WEBSTER AVENUE
 
2
EAST FORDHAM ROAD and VALENTINE AVENUE
 
2
EAST TREMONT AVENUE and WEBSTER AVENUE
 
2
Other values (1323)
1335 
ValueCountFrequency (%) 
EAST 184 STREET and JEROME AVENUE20.1%
 
EAST GUN HILL ROAD and GUNTHER AVENUE20.1%
 
EAST 168 STREET and WEBSTER AVENUE20.1%
 
EAST FORDHAM ROAD and VALENTINE AVENUE20.1%
 
EAST TREMONT AVENUE and WEBSTER AVENUE20.1%
 
EAST TREMONT AVENUE and WHITE PLAINS ROAD20.1%
 
HEATH AVENUE and WEST KINGSBRIDGE ROAD20.1%
 
EAST FORDHAM ROAD and MORRIS AVENUE20.1%
 
CROTONA AVENUE and CROTONA PARK NORTH20.1%
 
EAST 188 STREET and WEBSTER AVENUE20.1%
 
EAST FORDHAM ROAD and SOUTHERN BOULEVARD20.1%
 
SHERIDAN EXPRESSWAY and WESTCHESTER AVENUE20.1%
 
EAST 233 STREET and JEROME AVENUE20.1%
 
BRUCKNER BOULEVARD and EAST 149 STREET20.1%
 
EAST GUN HILL ROAD and WEBSTER AVENUE20.1%
 
EAST 149 STREET and EXTERIOR STREET20.1%
 
CROSS BRONX EXPRESSWAY and JEROME AVENUE20.1%
 
SEDGWICK AVENUE and WEST 181 STREET10.1%
 
ELLIS AVENUE and HAVEMEYER AVENUE10.1%
 
PURDY STREET and WESTCHESTER AVENUE10.1%
 
CO OP CITY BOULEVARD and PEARTREE AVENUE10.1%
 
COURTLANDT AVENUE and EAST 148 STREET10.1%
 
EAST 153 STREET and GERARD AVENUE10.1%
 
GRAND CONCOURSE and MARCY PLACE10.1%
 
HALLECK STREET and SPOFFORD AVENUE10.1%
 
Other values (1303)130396.9%
 
2020-12-12T15:15:25.822144image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1311 ?
Unique (%)97.5%
2020-12-12T15:15:25.908218image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length56
Median length35
Mean length35.18736059
Min length25

Overview of Unicode Properties

Unique unicode characters42
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
E703414.9%
 
40348.5%
 
A37057.8%
 
T32947.0%
 
R27735.9%
 
N26945.7%
 
S25685.4%
 
24565.2%
 
U18744.0%
 
O16663.5%
 
V16303.4%
 
a13452.8%
 
n13452.8%
 
d13452.8%
 
L11622.5%
 
D9131.9%
 
I8541.8%
 
C7121.5%
 
W6731.4%
 
H6171.3%
 
B5851.2%
 
P4841.0%
 
14711.0%
 
M4561.0%
 
G3760.8%
 
Other values (17)22614.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter3506874.1%
 
Lowercase Letter40358.5%
 
Space Separator40348.5%
 
Control24565.2%
 
Decimal Number17333.7%
 
Other Punctuation1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
147127.2%
 
225614.8%
 
322312.9%
 
61589.1%
 
71508.7%
 
81257.2%
 
41086.2%
 
9965.5%
 
5895.1%
 
0573.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
4034100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E703420.1%
 
A370510.6%
 
T32949.4%
 
R27737.9%
 
N26947.7%
 
S25687.3%
 
U18745.3%
 
O16664.8%
 
V16304.6%
 
L11623.3%
 
D9132.6%
 
I8542.4%
 
C7122.0%
 
W6731.9%
 
H6171.8%
 
B5851.7%
 
P4841.4%
 
M4561.3%
 
G3761.1%
 
Y3170.9%
 
K2870.8%
 
F1690.5%
 
X1350.4%
 
J730.2%
 
Z13< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a134533.3%
 
n134533.3%
 
d134533.3%
 

Most frequent Control characters

ValueCountFrequency (%) 
2456100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
'1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3910382.6%
 
Common822417.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
403449.1%
 
245629.9%
 
14715.7%
 
22563.1%
 
32232.7%
 
61581.9%
 
71501.8%
 
81251.5%
 
41081.3%
 
9961.2%
 
5891.1%
 
0570.7%
 
'1< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E703418.0%
 
A37059.5%
 
T32948.4%
 
R27737.1%
 
N26946.9%
 
S25686.6%
 
U18744.8%
 
O16664.3%
 
V16304.2%
 
a13453.4%
 
n13453.4%
 
d13453.4%
 
L11623.0%
 
D9132.3%
 
I8542.2%
 
C7121.8%
 
W6731.7%
 
H6171.6%
 
B5851.5%
 
P4841.2%
 
M4561.2%
 
G3761.0%
 
Y3170.8%
 
K2870.7%
 
F1690.4%
 
Other values (4)2250.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII47327100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
E703414.9%
 
40348.5%
 
A37057.8%
 
T32947.0%
 
R27735.9%
 
N26945.7%
 
S25685.4%
 
24565.2%
 
U18744.0%
 
O16663.5%
 
V16303.4%
 
a13452.8%
 
n13452.8%
 
d13452.8%
 
L11622.5%
 
D9131.9%
 
I8541.8%
 
C7121.5%
 
W6731.4%
 
H6171.3%
 
B5851.2%
 
P4841.0%
 
14711.0%
 
M4561.0%
 
G3760.8%
 
Other values (17)22614.8%
 

Number of Accidents
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3866171
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size10.6 KiB
2020-12-12T15:15:25.975275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9310129164
Coefficient of variation (CV)0.6714275456
Kurtosis34.56232458
Mean1.3866171
Median Absolute Deviation (MAD)0
Skewness4.67884777
Sum1865
Variance0.8667850505
MonotocityNot monotonic
2020-12-12T15:15:26.029822image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
1101975.8%
 
222416.7%
 
3644.8%
 
4151.1%
 
5120.9%
 
740.3%
 
630.2%
 
1220.1%
 
820.1%
 
ValueCountFrequency (%) 
1101975.8%
 
222416.7%
 
3644.8%
 
4151.1%
 
5120.9%
 
630.2%
 
740.3%
 
820.1%
 
1220.1%
 
ValueCountFrequency (%) 
1220.1%
 
820.1%
 
740.3%
 
630.2%
 
5120.9%
 
4151.1%
 
3644.8%
 
222416.7%
 
1101975.8%
 

PersonsInvolved(*)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct20
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.054275093
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Memory size10.6 KiB
2020-12-12T15:15:26.088873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q34
95-th percentile7
Maximum36
Range35
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.332648377
Coefficient of variation (CV)0.7637322461
Kurtosis43.7161995
Mean3.054275093
Median Absolute Deviation (MAD)0
Skewness4.954274383
Sum4108
Variance5.441248451
MonotocityNot monotonic
2020-12-12T15:15:26.148925image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
282761.5%
 
418914.1%
 
31057.8%
 
5584.3%
 
6544.0%
 
1433.2%
 
7171.3%
 
9120.9%
 
10100.7%
 
880.6%
 
1260.4%
 
1140.3%
 
1330.2%
 
1520.1%
 
1620.1%
 
2510.1%
 
1710.1%
 
1910.1%
 
2110.1%
 
3610.1%
 
ValueCountFrequency (%) 
1433.2%
 
282761.5%
 
31057.8%
 
418914.1%
 
5584.3%
 
6544.0%
 
7171.3%
 
880.6%
 
9120.9%
 
10100.7%
 
ValueCountFrequency (%) 
3610.1%
 
2510.1%
 
2110.1%
 
1910.1%
 
1710.1%
 
1620.1%
 
1520.1%
 
1330.2%
 
1260.4%
 
1140.3%
 
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
940 
1
367 
2
 
34
3
 
4
ValueCountFrequency (%) 
094069.9%
 
136727.3%
 
2342.5%
 
340.3%
 
2020-12-12T15:15:26.215482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:15:26.257018image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:26.306560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
094069.9%
 
136727.3%
 
2342.5%
 
340.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1345100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
094069.9%
 
136727.3%
 
2342.5%
 
340.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1345100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
094069.9%
 
136727.3%
 
2342.5%
 
340.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1345100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
094069.9%
 
136727.3%
 
2342.5%
 
340.3%
 

Injured Motorist
Real number (ℝ≥0)

ZEROS

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1568773234
Minimum0
Maximum4
Zeros1170
Zeros (%)87.0%
Memory size10.6 KiB
2020-12-12T15:15:26.361608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4414354331
Coefficient of variation (CV)2.813889372
Kurtosis12.72368456
Mean0.1568773234
Median Absolute Deviation (MAD)0
Skewness3.261873833
Sum211
Variance0.1948652416
MonotocityNot monotonic
2020-12-12T15:15:26.416655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
0117087.0%
 
114410.7%
 
2272.0%
 
330.2%
 
410.1%
 
ValueCountFrequency (%) 
0117087.0%
 
114410.7%
 
2272.0%
 
330.2%
 
410.1%
 
ValueCountFrequency (%) 
410.1%
 
330.2%
 
2272.0%
 
114410.7%
 
0117087.0%
 

Injured Passengers
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2141263941
Minimum0
Maximum11
Zeros1171
Zeros (%)87.1%
Memory size10.6 KiB
2020-12-12T15:15:26.478208image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7341234237
Coefficient of variation (CV)3.42845835
Kurtosis57.51827976
Mean0.2141263941
Median Absolute Deviation (MAD)0
Skewness6.161502572
Sum288
Variance0.5389372013
MonotocityNot monotonic
2020-12-12T15:15:26.531754image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
0117187.1%
 
11198.8%
 
2251.9%
 
3181.3%
 
460.4%
 
530.2%
 
1110.1%
 
810.1%
 
710.1%
 
ValueCountFrequency (%) 
0117187.1%
 
11198.8%
 
2251.9%
 
3181.3%
 
460.4%
 
530.2%
 
710.1%
 
810.1%
 
1110.1%
 
ValueCountFrequency (%) 
1110.1%
 
810.1%
 
710.1%
 
530.2%
 
460.4%
 
3181.3%
 
2251.9%
 
11198.8%
 
0117187.1%
 

Injured Cyclists
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1308 
1
 
36
2
 
1
ValueCountFrequency (%) 
0130897.2%
 
1362.7%
 
210.1%
 
2020-12-12T15:15:26.596810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-12-12T15:15:26.639847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:26.684886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0130897.2%
 
1362.7%
 
210.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1345100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0130897.2%
 
1362.7%
 
210.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1345100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0130897.2%
 
1362.7%
 
210.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1345100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0130897.2%
 
1362.7%
 
210.1%
 
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1226 
1
 
110
2
 
8
3
 
1
ValueCountFrequency (%) 
0122691.2%
 
11108.2%
 
280.6%
 
310.1%
 
2020-12-12T15:15:26.747440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-12-12T15:15:26.790477image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:26.839019image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0122691.2%
 
11108.2%
 
280.6%
 
310.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1345100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0122691.2%
 
11108.2%
 
280.6%
 
310.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1345100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0122691.2%
 
11108.2%
 
280.6%
 
310.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1345100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0122691.2%
 
11108.2%
 
280.6%
 
310.1%
 

Total Injured
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)0.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.4925595238
Minimum0
Maximum15
Zeros940
Zeros (%)69.9%
Memory size10.6 KiB
2020-12-12T15:15:26.894066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.043337047
Coefficient of variation (CV)2.11819485
Kurtosis37.62682736
Mean0.4925595238
Median Absolute Deviation (MAD)0
Skewness4.516796762
Sum662
Variance1.088552193
MonotocityNot monotonic
2020-12-12T15:15:26.948613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
094069.9%
 
126920.0%
 
2775.7%
 
3312.3%
 
4131.0%
 
570.5%
 
740.3%
 
820.1%
 
1510.1%
 
(Missing)10.1%
 
ValueCountFrequency (%) 
094069.9%
 
126920.0%
 
2775.7%
 
3312.3%
 
4131.0%
 
570.5%
 
740.3%
 
820.1%
 
1510.1%
 
ValueCountFrequency (%) 
1510.1%
 
820.1%
 
740.3%
 
570.5%
 
4131.0%
 
3312.3%
 
2775.7%
 
126920.0%
 
094069.9%
 

Killed Motorist
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1345 
ValueCountFrequency (%) 
01345100.0%
 
2020-12-12T15:15:26.992651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Killed passengers
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1345 
ValueCountFrequency (%) 
01345100.0%
 
2020-12-12T15:15:27.012168image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Killed Cyclists
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1345 
ValueCountFrequency (%) 
01345100.0%
 
2020-12-12T15:15:27.031184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Killed Pedestrians
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1345 
ValueCountFrequency (%) 
01345100.0%
 
2020-12-12T15:15:27.050200image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Total Killed
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1345 
ValueCountFrequency (%) 
01345100.0%
 
2020-12-12T15:15:27.069216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Contributing Factors (**)
Categorical

HIGH CARDINALITY
MISSING

Distinct105
Distinct (%)19.1%
Missing796
Missing (%)59.2%
Memory size10.6 KiB
Driver inattention/distraction 1
106 
Failure to yield right-of-way 1
51 
Backing unsafely 1
36 
Driver inattention/distraction 2
33 
Following too closely 1
32 
Other values (100)
291 
ValueCountFrequency (%) 
Driver inattention/distraction 11067.9%
 
Failure to yield right-of-way 1513.8%
 
Backing unsafely 1362.7%
 
Driver inattention/distraction 2332.5%
 
Following too closely 1322.4%
 
Turning improperly 1231.7%
 
Driver inexperience 1211.6%
 
Alcohol involvement 1191.4%
 
Unsafe lane changing 1161.2%
 
Other uninvolved vehicle 1151.1%
 
Unsafe speed 1141.0%
 
Traffic control disregarded 1141.0%
 
Passing or lane usage improper 1100.7%
 
Err/Confusn ped/Bike/Other ped 1100.7%
 
Passenger distraction 190.7%
 
Driver inattention/distraction 1 Following too closely 160.4%
 
Failure to yield right-of-way 260.4%
 
Following too closely 260.4%
 
Driver inattention/distraction 350.4%
 
Driver inattention/distraction 1 Failure to yield right-of-way 150.4%
 
Backing unsafely 1 Following too closely 140.3%
 
Driver inattention/distraction 2 Following too closely 130.2%
 
Driver inattention/distraction 1 Traffic control disregarded 130.2%
 
Unsafe lane changing 230.2%
 
Driver inattention/distraction 1 Passing or lane usage improper 130.2%
 
Other values (80)967.1%
 
(Missing)79659.2%
 
2020-12-12T15:15:27.127267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique65 ?
Unique (%)11.8%
2020-12-12T15:15:27.218845image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length157
Median length3
Mean length19.82304833
Min length3

Overview of Unicode Properties

Unique unicode characters45
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
870032.6%
 
n303811.4%
 
i17566.6%
 
a17246.5%
 
e13545.1%
 
t13154.9%
 
r12224.6%
 
o11514.3%
 
l7412.8%
 
s5612.1%
 
15612.1%
 
c4901.8%
 
d4371.6%
 
g3971.5%
 
v3511.3%
 
y3201.2%
 
u2440.9%
 
f2410.9%
 
/2300.9%
 
D2220.8%
 
p2100.8%
 
h2030.8%
 
-1640.6%
 
F1530.6%
 
w1470.6%
 
Other values (20)7302.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1609660.4%
 
Space Separator870032.6%
 
Uppercase Letter6892.6%
 
Decimal Number6532.4%
 
Other Punctuation2300.9%
 
Dash Punctuation1640.6%
 
Control1280.5%
 
Open Punctuation1< 0.1%
 
Close Punctuation1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
D22232.2%
 
F15322.2%
 
B639.1%
 
T618.9%
 
U507.3%
 
P405.8%
 
O385.5%
 
A324.6%
 
E121.7%
 
C121.7%
 
I40.6%
 
L20.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n303818.9%
 
i175610.9%
 
a172410.7%
 
e13548.4%
 
t13158.2%
 
r12227.6%
 
o11517.2%
 
l7414.6%
 
s5613.5%
 
c4903.0%
 
d4372.7%
 
g3972.5%
 
v3512.2%
 
y3202.0%
 
u2441.5%
 
f2411.5%
 
p2101.3%
 
h2031.3%
 
w1470.9%
 
m950.6%
 
k670.4%
 
x310.2%
 
b1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
8700100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/230100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
156185.9%
 
28412.9%
 
371.1%
 
410.2%
 

Most frequent Control characters

ValueCountFrequency (%) 
128100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-164100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(1100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1678563.0%
 
Common987737.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n303818.1%
 
i175610.5%
 
a172410.3%
 
e13548.1%
 
t13157.8%
 
r12227.3%
 
o11516.9%
 
l7414.4%
 
s5613.3%
 
c4902.9%
 
d4372.6%
 
g3972.4%
 
v3512.1%
 
y3201.9%
 
u2441.5%
 
f2411.4%
 
D2221.3%
 
p2101.3%
 
h2031.2%
 
F1530.9%
 
w1470.9%
 
m950.6%
 
k670.4%
 
B630.4%
 
T610.4%
 
Other values (10)2221.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
870088.1%
 
15615.7%
 
/2302.3%
 
-1641.7%
 
1281.3%
 
2840.9%
 
370.1%
 
41< 0.1%
 
(1< 0.1%
 
)1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII26662100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
870032.6%
 
n303811.4%
 
i17566.6%
 
a17246.5%
 
e13545.1%
 
t13154.9%
 
r12224.6%
 
o11514.3%
 
l7412.8%
 
s5612.1%
 
15612.1%
 
c4901.8%
 
d4371.6%
 
g3971.5%
 
v3511.3%
 
y3201.2%
 
u2440.9%
 
f2410.9%
 
/2300.9%
 
D2220.8%
 
p2100.8%
 
h2030.8%
 
-1640.6%
 
F1530.6%
 
w1470.6%
 
Other values (20)7302.7%
 

Bicycle
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1304 
1
 
40
2
 
1
ValueCountFrequency (%) 
0130497.0%
 
1403.0%
 
210.1%
 
2020-12-12T15:15:27.300916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-12-12T15:15:27.344954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:27.390493image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0130497.0%
 
1403.0%
 
210.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1345100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0130497.0%
 
1403.0%
 
210.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1345100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0130497.0%
 
1403.0%
 
210.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1345100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0130497.0%
 
1403.0%
 
210.1%
 

Bus
Categorical

Distinct3
Distinct (%)0.2%
Missing2
Missing (%)0.1%
Memory size10.6 KiB
0
1316 
1
 
26
2
 
1
ValueCountFrequency (%) 
0131697.8%
 
1261.9%
 
210.1%
 
(Missing)20.1%
 
2020-12-12T15:15:27.454548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-12-12T15:15:27.497585image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:27.543124image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0265965.9%
 
.134333.3%
 
1260.6%
 
n40.1%
 
a2< 0.1%
 
21< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number268666.6%
 
Other Punctuation134333.3%
 
Lowercase Letter60.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0265999.0%
 
1261.0%
 
21< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1343100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n466.7%
 
a233.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common402999.9%
 
Latin60.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0265966.0%
 
.134333.3%
 
1260.6%
 
21< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n466.7%
 
a233.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4035100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0265965.9%
 
.134333.3%
 
1260.6%
 
n40.1%
 
a2< 0.1%
 
21< 0.1%
 

Fire Truck
Categorical

Distinct4
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size10.6 KiB
0
1259 
1
 
83
3
 
1
2
 
1
ValueCountFrequency (%) 
0125993.6%
 
1836.2%
 
310.1%
 
210.1%
 
(Missing)10.1%
 
2020-12-12T15:15:27.605678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.1%
2020-12-12T15:15:27.648215image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:27.696756image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters7
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0260364.5%
 
.134433.3%
 
1832.1%
 
n2< 0.1%
 
a1< 0.1%
 
21< 0.1%
 
31< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number268866.6%
 
Other Punctuation134433.3%
 
Lowercase Letter30.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0260396.8%
 
1833.1%
 
21< 0.1%
 
31< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1344100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n266.7%
 
a133.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common403299.9%
 
Latin30.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0260364.6%
 
.134433.3%
 
1832.1%
 
21< 0.1%
 
31< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n266.7%
 
a133.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4035100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0260364.5%
 
.134433.3%
 
1832.1%
 
n2< 0.1%
 
a1< 0.1%
 
21< 0.1%
 
31< 0.1%
 
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1296 
1
 
44
2
 
5
ValueCountFrequency (%) 
0129696.4%
 
1443.3%
 
250.4%
 
2020-12-12T15:15:27.760812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:15:27.805851image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:27.851890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0129696.4%
 
1443.3%
 
250.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1345100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0129696.4%
 
1443.3%
 
250.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1345100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0129696.4%
 
1443.3%
 
250.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1345100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0129696.4%
 
1443.3%
 
250.4%
 

Livery vehicle
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1276 
1
 
57
2
 
11
4
 
1
ValueCountFrequency (%) 
0127694.9%
 
1574.2%
 
2110.8%
 
410.1%
 
2020-12-12T15:15:27.914444image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-12-12T15:15:27.956980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:28.005022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0127694.9%
 
1574.2%
 
2110.8%
 
410.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1345100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0127694.9%
 
1574.2%
 
2110.8%
 
410.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1345100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0127694.9%
 
1574.2%
 
2110.8%
 
410.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1345100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0127694.9%
 
1574.2%
 
2110.8%
 
410.1%
 

Motorcycle
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1345 
ValueCountFrequency (%) 
01345100.0%
 
2020-12-12T15:15:28.045057image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Other
Real number (ℝ≥0)

ZEROS

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03420074349
Minimum0
Maximum4
Zeros1306
Zeros (%)97.1%
Memory size10.6 KiB
2020-12-12T15:15:28.079086image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2223166142
Coefficient of variation (CV)6.500344479
Kurtosis114.7984038
Mean0.03420074349
Median Absolute Deviation (MAD)0
Skewness9.164001291
Sum46
Variance0.04942467693
MonotocityNot monotonic
2020-12-12T15:15:28.134133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
0130697.1%
 
1352.6%
 
220.1%
 
410.1%
 
310.1%
 
ValueCountFrequency (%) 
0130697.1%
 
1352.6%
 
220.1%
 
310.1%
 
410.1%
 
ValueCountFrequency (%) 
410.1%
 
310.1%
 
220.1%
 
1352.6%
 
0130697.1%
 

Passenger vehicle
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.481784387
Minimum0
Maximum10
Zeros269
Zeros (%)20.0%
Memory size10.6 KiB
2020-12-12T15:15:28.194685image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.24217902
Coefficient of variation (CV)0.8382994392
Kurtosis5.028716937
Mean1.481784387
Median Absolute Deviation (MAD)1
Skewness1.563720642
Sum1993
Variance1.543008718
MonotocityNot monotonic
2020-12-12T15:15:28.251234image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
149336.7%
 
239329.2%
 
026920.0%
 
31108.2%
 
4493.6%
 
6120.9%
 
5120.9%
 
730.2%
 
820.1%
 
1010.1%
 
910.1%
 
ValueCountFrequency (%) 
026920.0%
 
149336.7%
 
239329.2%
 
31108.2%
 
4493.6%
 
5120.9%
 
6120.9%
 
730.2%
 
820.1%
 
910.1%
 
ValueCountFrequency (%) 
1010.1%
 
910.1%
 
820.1%
 
730.2%
 
6120.9%
 
5120.9%
 
4493.6%
 
31108.2%
 
239329.2%
 
149336.7%
 

Pick-up truck
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1316 
1
 
28
2
 
1
ValueCountFrequency (%) 
0131697.8%
 
1282.1%
 
210.1%
 
2020-12-12T15:15:28.317291image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-12-12T15:15:28.361328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:28.406868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0131697.8%
 
1282.1%
 
210.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1345100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0131697.8%
 
1282.1%
 
210.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1345100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0131697.8%
 
1282.1%
 
210.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1345100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0131697.8%
 
1282.1%
 
210.1%
 
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1295 
1
 
46
2
 
4
ValueCountFrequency (%) 
0129596.3%
 
1463.4%
 
240.3%
 
2020-12-12T15:15:28.470923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:15:28.514460image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:28.559499image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0129596.3%
 
1463.4%
 
240.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1345100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0129596.3%
 
1463.4%
 
240.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1345100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0129596.3%
 
1463.4%
 
240.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1345100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0129596.3%
 
1463.4%
 
240.3%
 

SUV\Station Wagon
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4104089219
Minimum0
Maximum6
Zeros923
Zeros (%)68.6%
Memory size10.6 KiB
2020-12-12T15:15:28.612545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7036545097
Coefficient of variation (CV)1.714520499
Kurtosis6.464558585
Mean0.4104089219
Median Absolute Deviation (MAD)0
Skewness2.12959211
Sum552
Variance0.495129669
MonotocityNot monotonic
2020-12-12T15:15:28.662087image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
092368.6%
 
132023.8%
 
2836.2%
 
3120.9%
 
460.4%
 
610.1%
 
ValueCountFrequency (%) 
092368.6%
 
132023.8%
 
2836.2%
 
3120.9%
 
460.4%
 
610.1%
 
ValueCountFrequency (%) 
610.1%
 
460.4%
 
3120.9%
 
2836.2%
 
132023.8%
 
092368.6%
 

Taxi vehicle
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1345 
ValueCountFrequency (%) 
01345100.0%
 
2020-12-12T15:15:28.702122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Unknown
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1130 
1
194 
2
 
17
3
 
4
ValueCountFrequency (%) 
0113084.0%
 
119414.4%
 
2171.3%
 
340.3%
 
2020-12-12T15:15:28.743157image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:15:28.784693image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:28.833235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0113084.0%
 
119414.4%
 
2171.3%
 
340.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1345100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0113084.0%
 
119414.4%
 
2171.3%
 
340.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1345100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0113084.0%
 
119414.4%
 
2171.3%
 
340.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1345100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0113084.0%
 
119414.4%
 
2171.3%
 
340.3%
 

Van
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
1255 
1
 
85
2
 
4
3
 
1
ValueCountFrequency (%) 
0125593.3%
 
1856.3%
 
240.3%
 
310.1%
 
2020-12-12T15:15:28.895788image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-12-12T15:15:28.938325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:28.986867image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0125593.3%
 
1856.3%
 
240.3%
 
310.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1345100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0125593.3%
 
1856.3%
 
240.3%
 
310.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1345100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0125593.3%
 
1856.3%
 
240.3%
 
310.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1345100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0125593.3%
 
1856.3%
 
240.3%
 
310.1%
 

Location 1
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1248
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
POINT (-73.866667 40.85)
 
9
POINT (-73.923391 40.837947)
 
4
POINT (-73.921176 40.811493)
 
4
POINT (-73.869079 40.831102)
 
4
POINT (-73.914721 40.844376)
 
3
Other values (1243)
1321 
ValueCountFrequency (%) 
POINT (-73.866667 40.85)90.7%
 
POINT (-73.923391 40.837947)40.3%
 
POINT (-73.921176 40.811493)40.3%
 
POINT (-73.869079 40.831102)40.3%
 
POINT (-73.914721 40.844376)30.2%
 
POINT (-73.899642 40.886573)30.2%
 
POINT (-73.880197 40.895214)30.2%
 
POINT (-73.871222 40.865562)30.2%
 
POINT (-73.902539 40.860637)30.2%
 
POINT (-73.871633 40.87867)30.2%
 
POINT (-73.8706 40.865401)30.2%
 
POINT (-73.856663 40.858107)20.1%
 
POINT (-73.881307 40.882756)20.1%
 
POINT (-73.908375 40.862827)20.1%
 
POINT (-73.927269 40.811097)20.1%
 
POINT (-73.929195 40.809543)20.1%
 
POINT (-73.891124 40.860018)20.1%
 
POINT (-73.921686 40.838925)20.1%
 
POINT (-73.827926 40.886037)20.1%
 
POINT (-73.835851 40.865056)20.1%
 
POINT (-73.894072 40.842208)20.1%
 
POINT (-73.912932 40.814269)20.1%
 
POINT (-73.86815 40.83136)20.1%
 
POINT (-73.860835 40.833156)20.1%
 
POINT (-73.917655 40.840245)20.1%
 
Other values (1223)127594.8%
 
2020-12-12T15:15:29.067936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1171 ?
Unique (%)87.1%
2020-12-12T15:15:29.148506image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length28
Median length28
Mean length27.72565056
Min length24

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
834709.3%
 
327077.3%
 
26907.2%
 
.26907.2%
 
426887.2%
 
726527.1%
 
023656.3%
 
917754.8%
 
614103.8%
 
213823.7%
 
113513.6%
 
513513.6%
 
P13453.6%
 
O13453.6%
 
I13453.6%
 
N13453.6%
 
T13453.6%
 
(13453.6%
 
-13453.6%
 
)13453.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number2115156.7%
 
Uppercase Letter672518.0%
 
Space Separator26907.2%
 
Other Punctuation26907.2%
 
Open Punctuation13453.6%
 
Dash Punctuation13453.6%
 
Close Punctuation13453.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
P134520.0%
 
O134520.0%
 
I134520.0%
 
N134520.0%
 
T134520.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2690100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(1345100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1345100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
8347016.4%
 
3270712.8%
 
4268812.7%
 
7265212.5%
 
0236511.2%
 
917758.4%
 
614106.7%
 
213826.5%
 
113516.4%
 
513516.4%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.2690100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)1345100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3056682.0%
 
Latin672518.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
P134520.0%
 
O134520.0%
 
I134520.0%
 
N134520.0%
 
T134520.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
8347011.4%
 
327078.9%
 
26908.8%
 
.26908.8%
 
426888.8%
 
726528.7%
 
023657.7%
 
917755.8%
 
614104.6%
 
213824.5%
 
113514.4%
 
513514.4%
 
(13454.4%
 
-13454.4%
 
)13454.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII37291100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
834709.3%
 
327077.3%
 
26907.2%
 
.26907.2%
 
426887.2%
 
726527.1%
 
023656.3%
 
917754.8%
 
614103.8%
 
213823.7%
 
113513.6%
 
513513.6%
 
P13453.6%
 
O13453.6%
 
I13453.6%
 
N13453.6%
 
T13453.6%
 
(13453.6%
 
-13453.6%
 
)13453.6%
 

Location 1 (city)
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
Bronx
1345 
ValueCountFrequency (%) 
Bronx1345100.0%
 
2020-12-12T15:15:29.213562image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:15:29.251595image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:29.290628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length5
Min length5

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
B134520.0%
 
r134520.0%
 
o134520.0%
 
n134520.0%
 
x134520.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter538080.0%
 
Uppercase Letter134520.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B1345100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
r134525.0%
 
o134525.0%
 
n134525.0%
 
x134525.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin6725100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
B134520.0%
 
r134520.0%
 
o134520.0%
 
n134520.0%
 
x134520.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6725100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
B134520.0%
 
r134520.0%
 
o134520.0%
 
n134520.0%
 
x134520.0%
 

Location 1 (state)
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
NY
1345 
ValueCountFrequency (%) 
NY1345100.0%
 
2020-12-12T15:15:29.351180image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:15:29.389213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:29.428246image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N134550.0%
 
Y134550.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter2690100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N134550.0%
 
Y134550.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2690100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N134550.0%
 
Y134550.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2690100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N134550.0%
 
Y134550.0%
 

Interactions

2020-12-12T15:15:20.043671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:20.112230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:20.181790image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:20.254853image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:20.322411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:20.392972image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:20.464032image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:20.531090image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:20.598648image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:20.669710image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:20.743773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:20.820339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:20.892401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:20.965965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:21.041530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:21.112090image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:21.184653image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:21.258716image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:21.337284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:21.416852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:21.490416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:21.566982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:21.645049image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:21.719112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:21.795678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:21.863237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:21.933297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:22.005859image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:22.072417image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:22.140976image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:22.212037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:22.278094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:22.347153image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:22.417213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:22.490777image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:22.565341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:22.635401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:22.707963image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:22.785030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:22.855591image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:22.927152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:23.001216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:23.077281image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:23.155349image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:23.228411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:23.305478image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:23.384045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:23.459610image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:23.533674image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:23.600231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:23.669291image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:23.741353image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:23.809412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:23.879472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:23.952034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:24.019092image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:24.087651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:24.157211image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:24.229773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:24.304338image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:24.374398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:24.448461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:24.521024image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:24.589583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T15:15:29.509817image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T15:15:29.710990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T15:15:29.912163image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T15:15:30.106330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T15:15:35.949859image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T15:15:24.781248image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:25.366251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:25.514379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:15:25.604956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

Intersection AddressNumber of AccidentsPersonsInvolved(*)Accidents with InjuriesInjured MotoristInjured PassengersInjured CyclistsInjured PedestriansTotal InjuredKilled MotoristKilled passengersKilled CyclistsKilled PedestriansTotal KilledContributing Factors (**)BicycleBusFire TruckLarge com veh(6 or more tires)Livery vehicleMotorcycleOtherPassenger vehiclePick-up truckSmall com veh(4tires)SUV\Station WagonTaxi vehicleUnknownVanLocation 1Location 1 (city)Location 1 (state)
03 AVENUE and BRUCKNER BOULEVARD12000000.000000Driver inattention/distraction 100.00.010001000000POINT (-73.929441 40.807647)BronxNY
13 AVENUE\nand\nEAST 135 STREET36000000.000000Driver inattention/distraction 1\nTraffic control disregarded 100.00.000000000000POINT (-73.929195 40.809543)BronxNY
23 AVENUE\nand\nEAST 137 STREET12000000.000000Driver inattention/distraction 100.00.002000000000POINT (-73.927819 40.810602)BronxNY
33 AVENUE\nand\nEAST 138 STREET23100011.000000NaN10.00.000000002000POINT (-73.927269 40.811097)BronxNY
43 AVENUE\nand\nEAST 142 STREET12100101.000000NaN10.00.000000000010POINT (-73.923746 40.812853)BronxNY
53 AVENUE\nand\nEAST 143 STREET12000000.000000Following too closely 100.00.000001010000POINT (-73.922862 40.813321)BronxNY
63 AVENUE\nand\nEAST 144 STREET25111002.000000Driver inattention/distraction 1\nFailure to yield right-of-way 100.00.000004000000POINT (-73.922082 40.813721)BronxNY
73 AVENUE\nand\nEAST 147 STREET23000000.000000Driver inattention/distraction 100.00.001001001000POINT (-73.919438 40.815067)BronxNY
83 AVENUE\nand EAST 149 STREET35210012.000000Failure to yield right-of-way 1\nPassenger distraction 1 Fatigued/drowsy 100.00.010002001000POINT (-73.917624 40.816062)BronxNY
9ALEXANDER AVENUE\nand BRUCKNER BOULEVARD12000000.000000Other uninvolved vehicle 200.00.000002000000POINT (-73.927487 40.806857)BronxNY

Last rows

Intersection AddressNumber of AccidentsPersonsInvolved(*)Accidents with InjuriesInjured MotoristInjured PassengersInjured CyclistsInjured PedestriansTotal InjuredKilled MotoristKilled passengersKilled CyclistsKilled PedestriansTotal KilledContributing Factors (**)BicycleBusFire TruckLarge com veh(6 or more tires)Livery vehicleMotorcycleOtherPassenger vehiclePick-up truckSmall com veh(4tires)SUV\Station WagonTaxi vehicleUnknownVanLocation 1Location 1 (city)Location 1 (state)
1335RESERVOIR OVAL WEST\nand\nVAN CORTLANDT\nAVENUE EAST12000000.000000Driver inattention/distraction 100.00.000001001000POINT (-73.879449 40.876877)BronxNY
1336SEDGWICK AVENUE and\nWEBB AVENUE24000000.000000Backing unsafely 1\nFailure to yield right-of-way 1\nTurning improperly 100.00.000004000000POINT (-73.90907 40.863157)BronxNY
1337SEDGWICK AVENUE and\nWEST 183 STREET12000000.000000NaN00.00.000000000020POINT (-73.910933 40.860659)BronxNY
1338SEDGWICK AVENUE and\nWEST FORDHAM ROAD612100101.000000NaN00.00.000000000000POINT (-73.909057 40.863059)BronxNY
1339UNIVERSITY AVENUE\nand\nWEST 181 STREET14121003.000000NaN00.00.000003000000POINT (-73.90974 40.857197)BronxNY
1340UNIVERSITY AVENUE\nand\nWEST 188 STREET12000000.000000NaN00.00.000002000000POINT (-73.903896 40.864435)BronxNY
1341UNIVERSITY AVENUE\nand\nWEST 190 STREET12100011.000000NaN00.00.000000001000POINT (-73.903315 40.865356)BronxNY
1342UNIVERSITY AVENUE\nand\nWEST 192 STREET12000000.000000Driver inexperience 100.01.000000001001POINT (-73.902054 40.867039)BronxNY
1343UNIVERSITY AVENUE\nand\nWEST FORDHAM\nROAD37000000.000000Driver inattention/distraction 1\nTurning improperly 100.01.000005000011POINT (-73.904917 40.862794)BronxNY
1344WEBB AVENUE\nand\nWEST 188 STREET12000000.000000NaN00.00.000001000000POINT (-73.866667 40.85)BronxNY